Spillover Detection for Donor Selection in Synthetic Control Models
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AI 根据所提供的论文材料归类：摘要研究合成控制因果估计中的干预溢出及供体筛选，直接涉及因果推断与干扰。
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合成控制模型中用于供体选择的溢出检测
- 研究问题
- 当合成控制的候选供体可能受到干预溢出影响且候选池很大时，如何减少对先验领域知识的依赖并识别有效供体？
- 方法与思路
- 作者提出基于干预前数据识别供体干预后取值的理论框架，构造溢出检测与供体筛选，并用敏感性分析和近端因果、工具变量思路处理偏差。
- 材料陈述的贡献
- 摘要称，研究给出供体识别所需条件的定理、误选或误排供体的因果估计偏差界，并说明被排除供体仍可用于进一步去偏。
- 阅读关注点
- 应核对仅用干预前数据识别后期供体值的假设、检测阈值与误判控制，以及偏差界和利用被排除供体去偏的成立条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出定理假设及偏差界形式，无法判断其在具体应用中的可检验性。
原文摘要
Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC requires the existence of correlated units that are not impacted by the intervention. Given one of these potential donor units, how can we decide whether it is in fact a valid donor - that is, one not subject to spillover effects from the intervention? Such a decision typically requires appealing to strong a priori domain knowledge specifying the units, which becomes infeasible in situations with large pools of potential donors. In this paper, we introduce a practical, theoretically-grounded donor selection procedure, aiming to weaken this domain knowledge requirement. Our main result is a Theorem that yields the assumptions required to identify donor values at post-intervention time points using only pre-intervention data. We show how this Theorem - and the assumptions underpinning it - can be turned into a practical method for detecting potential spillover effects and excluding invalid donors when constructing SCs. Importantly, we employ sensitivity analysis to formally bound the bias in our SC causal estimate in situations where an excluded donor was indeed valid, or where a selected donor was invalid. Using ideas from the proximal causal inference and instrumental variables literature, we show that the excluded donors can nevertheless be leveraged to further debias causal effect estimates. Finally, we illustrate our donor selection procedure on both simulated and real-world datasets.